We’ve talked at length about the different technical and organizational factors that companies should consider before implementing advanced analytics. Let’s now walk through a hypothetical project, in which you and your team have decided to experiment with analytics for the logistics operation of your business.

Your direct report, the leader in the supply-chain area, is on board with the initiative and the logistic team seems engaged and ready. The senior leadership has decided that your team will work with a boutique consultancy that has a track record of success in implementing data analytic improvements in the logistics area. With goals that are well-defined, understood, and achievable, the project kicks off with enthusiasm from the internal team and the vendor.

Five weeks later, though, the project seems to have gone pear-shaped. Progress has been slow, and the schedule has slipped with several missed deadlines. The work continues to...